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1. Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering

   https://doi.org/10.1115/DETC2018-85941  

   Safarkhani, Salar ; Bilionis, Ilias ; Panchal, Jitesh H. ( August 2018 , ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores twomore »critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills.« less
2. A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design

   Safarkhani, Salar ; Kattakuri, Vikranth ; Bilionis, I. ; Panchal, Jitesh H. ( July 2018 , Council of Engineering Systems Universities (CESUN) Global Conference)

   We present a principal-agent model of a one-shot, shallow, systems engineering process. The process is "one-shot" in the sense that decisions are made during a one-time step and that they are final. The term "shallow" refers to a one-layer hierarchy of the process. Specifically, we assume that the systems engineer has already decomposed the problem in subsystems and that each subsystem is assigned to a different subsystem engineer. Each subsystem engineer works independently to maximize their own expected payoff. The goal of the systems engineer is to maximize the system-level payoff by incentivizing the subsystem engineers. We restrict our attention to requirements-based system-level payoffs, i.e., the systems engineer makes a profit only if all the design requirements are met. We illustrate the model using the design of an Earth-orbiting satellite system where the systems engineer determines the optimum incentive structures and requirements for two subsystems: the propulsion subsystem and the power subsystem. The model enables the analysis of a systems engineer's decisions about optimal passed-down requirements and incentives for sub-system engineers under different levels of task difficulty and associated costs. Sample results, for the case of risk-neutral systems and subsystems engineers, show that it is not always in the bestmore »interest of the systems engineer to pass down the true requirements. As expected, the model predicts that for small to moderate task uncertainties the optimal requirements are higher than the true ones, effectively eliminating the probability of failure for the systems engineer. In contrast, the model predicts that for large task uncertainties the optimal requirements should be smaller than the true ones in order to lure the subsystem engineers into participation.« less
3. Optimizing Gradual SDN Upgrades in ISP Networks

   Poularakis, K. ; Iosifidis, G. ; Smaragdakis, G. ; Tassiulas, L. ( February 2019 , IEEE/ACM transactions on networking)

   Nowadays, there is a fast-paced shift from legacy telecommunication systems to novel software-defined network (SDN) architectures that can support on-the-fly network reconfiguration, therefore, empowering advanced traffic engineering mechanisms. Despite this momentum, migration to SDN cannot be realized at once especially in high-end networks of Internet service providers (ISPs). It is expected that ISPs will gradually upgrade their networks to SDN over a period that spans several years. In this paper, we study the SDN upgrading problem in an ISP network: which nodes to upgrade and when we consider a general model that captures different migration costs and network topologies, and two plausible ISP objectives: 1) the maximization of the traffic that traverses at least one SDN node, and 2) the maximization of the number of dynamically selectable routing paths enabled by SDN nodes. We leverage the theory of submodular and supermodular functions to devise algorithms with provable approximation ratios for each objective. Using realworld network topologies and traffic matrices, we evaluate the performance of our algorithms and show up to 54% gains over state-of-the-art methods. Moreover, we describe the interplay between the two objectives; maximizing one may cause a factor of 2 loss to the other. We also study themore »dual upgrading problem, i.e., minimizing the upgrading cost for the ISP while ensuring specific performance goals. Our analysis shows that our proposed algorithm can achieve up to 2.5 times lower cost to ensure performance goals over state-of-the-art methods.« less
4. WIP: Online Tutorials to Help Undergraduates Bridge the Gap Between General Writing and Engineering Writing

   https://doi.org/10.18260/1-2--35563  

   Alley, Michael ; Garner, Joanna ; Pigeon, Kaitlyn ( June 2020 , WIP: Online Tutorials to Help Undergraduates Bridge the Gap Between General Writing and Engineering Writing)

   The demands of engineering writing are much different from those of general writing, which students study from grade school through first-year composition. First, the content of engineering writing is both more specific and more complex [1]. As a second difference, not only do the types of audiences vary more in engineering but so does the audience’s level of knowledge about the content. Yet a third difference is that the expected level of precision in engineering writing is much higher [2]. Still a fourth difference is that the formats for engineering reports, which call for writing in sections and for incorporating illustrations and equations, are much more detailed than the double-space essays of first-year composition. Because many engineering students do not take a technical writing course until their junior or senior year, a gap exists between what undergraduates have learned to do in general writing courses and what those students are expected to produce in design courses and laboratory courses. While some engineering colleges such as the University of Michigan have bridged the gap with instruction about engineering writing in first-year design, a few such as the University of Wisconsin-Madison have done so with first-year English [4]. Still, a third groupmore »of schools such as Purdue have done so using an integration of these courses [5]. Unfortunately, many other engineering colleges have not bridged the gap in the first year. For instance, at Penn State, first-year design is not an option for teaching engineering writing because this course spans only one semester course and has no room for another major instructional topic. In addition, at this same institution, first-year composition is not an option because the English Department is adamant about having that course’s scope remain on general writing. Although a technical writing course in the junior or senior year should theoretically bridge the gap, not understanding the differences between general writing and engineering writing poses problems for engineering students who have yet taken technical writing. For instance, not understanding the organization of an engineering report can significantly pull down a report’s grade and lead students to assume that they are inherently weak at engineering writing [6]. Another problem is that engineering students who have not bridged the gap between general writing and engineering writing are at a disadvantage when writing emails and reports during a summer internship. To bridge this gap, we have created an online resource [7] that teaches students the essential differences between general writing and the writing done by engineers. At the heart of the resource are two web pages—one on writing reports and the other on writing professional emails. Each page consists of a series of short films that provide the essential differences between the two types of writing and a quiz to ensure comprehension of the films. In addition, students have links to model documents, while faculty have links to lesson plans. Using an NSF I-Corps approach [8], which is an educational version of how to build a start-up company [9], we have developed our web resource over the past six months. Specifically, we have tested value propositions through customer interviews of faculty and students in first-year courses in which the resource has been piloted. Using the results of those customer interviews, we have revised our two web pages. This paper presents the following highlights of this effort: (1) our customer discoveries about the gap between general writing and engineering writing, (2) the corresponding pivots that we made in the online resource to respond to those discoveries, and (3) the website usage statistics that show the effects of making those pivots« less
5. Defining and Assessing Systems Thinking in Diverse Engineering Populations

   Mosyjowski, E.A. ; Daly, S.R. ; Lattuca, L. ( January 2019 , Proceedings of the American Society for Engineering Education Conference)

   Engineers are called to play an important role in addressing the complex problems of our global society, such as climate change and global health care. In order to adequately address these complex problems, engineers must be able to identify and incorporate into their decision making relevant aspects of systems in which their work is contextualized, a skill often referred to as systems thinking. However, within engineering, research on systems thinking tends to emphasize the ability to recognize potentially relevant constituent elements and parts of an engineering problem, rather than how these constituent elements and parts are embedded in broader economic, sociocultural, and temporal contexts and how all of these must inform decision making about problems and solutions. Additionally, some elements of systems thinking, such as an awareness of a particular sociocultural context or the coordination of work among members of a cross-disciplinary team, are not always recognized as core engineering skills, which alienates those whose strengths and passions are related to, for example, engineering systems that consider and impact social change. Studies show that women and minorities, groups underrepresented within engineering, are drawn to engineering in part for its potential to address important social issues. Emphasizing the importance of systemsmore »thinking and developing a more comprehensive definition of systems thinking that includes both constituent parts and contextual elements of a system will help students recognize the relevance and value of these other elements of engineering work and support full participation in engineering by a diverse group of students. We provide an overview of our study, in which we are examining systems thinking across a range of expertise to develop a scenario-based assessment tool that educators and researchers can use to evaluate engineering students’ systems thinking competence. Consistent with the aforementioned need to define and study systems thinking in a comprehensive, inclusive manner, we begin with a definition of systems thinking as a holistic approach to problem solving in which linkages and interactions of the immediate work with constituent parts, the larger sociocultural context, and potential impacts over time are identified and incorporated into decision making. In our study, we seek to address two key questions: 1) How do engineers of different levels of education and experience approach problems that require systems thinking? and 2) How do different types of life, educational, and work experiences relate to individuals’ demonstrated level of expertise in solving systems thinking problems? Our study is comprised of three phases. The first two phases include a semi-structured interview with engineering students and professionals about their experiences solving a problem requiring systems thinking and a think-aloud interview in which participants are asked to talk through how they would approach a given engineering scenario and later reflect on the experiences that inform their thinking. Data from these two phases will be used to develop a written assessment tool, which we will test by administering the written instrument to undergraduate and graduate engineering students in our third study phase. Our paper describes our study design and framing and includes preliminary findings from the first phase of our study.« less